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检索条件"任意字段=8th International Conference on Data Science and Machine Learning Applications, CDMA 2025"

共 824 条记录，以下是51-60 订阅

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An Efficient Federated learning Privacy Protection Algorithm based on Model Quantification 24

An Efficient Federated Learning Privacy Protection Algorithm...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Yufan Cao, Tianya Zhang, Yujing School of Computer Science and Engineering Northwest Normal University Gansu Lanzhou China

ISBN: (纸本)9798400710094

Federated learning, as a distributed machine learning method, enables data sets from different clients to train high-quality models without leaving the local area. However, in the process of federation learning to upload model parameters to the server, privacy leakage is easy to occur. At the same time, with the increase of training rounds, in order to achieve the expected effect, the client frequently uploads data to the server during each training, which will cause certain memory consumption. therefore, to solve the problem of privacy leakage and excessive memory consumption during client upload, this paper proposes an efficient federated learning privacy protection algorithm (FedEMQ) based on model quantification. the algorithm adds noise before the client uploads the model parameters to the server to protect the privacy of the client. the model parameters are then quantized to convert the floating-point data to a low-precision type to reduce its memory consumption. Finally, the experimental show that the system balances model performance and memory consumption well while protecting client privacy. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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the Next-Gen Interactive Runtime Simulator for Neural Network Programming 8

The Next-Gen Interactive Runtime Simulator for Neural Networ...

引用

8th international conference on the Art, science, and Engineering of Programming (Programming)

作者： Sanaullah, Sanaullah Attaullah, Hasina Jungeblut, thorsten Univ Appl Sci & Arts Bielefeld Germany

ISBN: (纸本)9798400706349

this abstract introduces a Next-Gen runtime tool research direction for neural network programming, which was presented in the Programming 2024 event. these runtime revolutionary tools allow users to conduct neural model evaluations and understand neuron behavior in run-time, neural network design, and training processes. By eliminating the need for extensive backend coding, one of the previously introduced runtime simulators called RAVSim, simplified the integration of custom datasets and enabled developers to focus on high-level tasks, accelerating advancements in artificial intelligence (AI) and computational neuroscience. With its innovative features and user-friendly interface, RAVSim supports researchers and developers in using SNNs full potential in diverse applications.

关键词： Next-Gen Tools Runtime Simulator machine learning Image Classification Neural Models No-Coding

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Emotion Recognition from WhatsApp Text Messages Using Unsupervised machine learning 8

Emotion Recognition from WhatsApp Text Messages Using Unsupe...

引用

8th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Padate, Roshni Gupta, Ashutosh Chakrabarti, Prasun Sharma, Arvind Fr. Conceicao Rodrigues College of Engineering Computer Engineering Mumbai India Sir Padampat Singhania University Computer Science and Engineering Udaipur India Sir Padampat Singhania University Department of Mathematics Udaipur India

ISBN: (纸本)9798350376425

With the widespread use of instant messaging applications like WhatsApp, there is an increasing demand for systems that can analyze and interpret user emotions based on textual communication. this research introduces a novel system for detecting emotions from WhatsApp text messages using unsupervised machine learning techniques. the system leverages natural language processing (NLP) methods to preprocess text data and applies a Pointwise Mutual Information (PMI)-based algorithm to classify messages into six basic emotion categories: joy, anger, sadness, fear, disgust, and surprise. the model's effectiveness is evaluated using a corpus of WhatsApp messages, demonstrating a precision rate of 92.75%. this approach shows promising results in handling the informal language and slang common in WhatsApp messages, offering significant potential for real-world applications in emotion detection from textual data. © 2024 IEEE.

关键词： Adversarial machine learning

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Regularization of machine learning and Linear Algebra 24

Regularization of Machine Learning and Linear Algebra

引用

8th international conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Liu, Shuang Kabanikhin, Sergey Igorevich Strijhak, Sergei Vladimirovich Novosibirsk State University Akademgorodok Novosibirsk Russia Sobolev Institute of Mathematics Siberian Branch of the Russian Academy of Sciences Novosibirsk Russia Ivannikov Institute of System Programming of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798400718182

this paper explores the connection between systems of linear algebraic equations (SLAE) and machine learning methods, including regularization techniques, to establish a more novel neural network model based on linear neural networks. the goal is to construct a weight matrix for the neural network, which, by simulating the process of finding pseudo-solutions to SLAE, can generate the optimal answer for any input data. In this new neural network model, linear operations are performed first, followed by nonlinear operations, ultimately yielding an optimized weight matrix that serves as the pseudo-solution to the SLAE. the paper demonstrates how linear neural networks can be simplified to SLAE, how adding nonlinear layers to the linear neural network model can improve accuracy, and how machine learning methods can be used to find pseudo-solutions to SLAE. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Effectiveness of Security Export Control Ontology for Predicting Answer Type and Regulation Categories 24

Effectiveness of Security Export Control Ontology for Predic...

引用

8th international conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Rzepka, Rafal Obayashi, Akihiko Faculty of Information Science and Technology Hokkaido University Sapporo Japan Faculty of Engineering Hokkaido University Sapporo Japan

ISBN: (纸本)9798400718014

In this paper we present results of our experiments investigating if an expert knowledge graph can improve Large Language Models accuracy in predicting correct answer labels and regulations related to the topic of security export control. As the lack of related data prevents machine-learning or fine-tuning approaches, we implement prompt expansion by searching most relevant nodes of the graph and adding the expanded context to the prompt. Results of our experiments show that the addition improved answer type selection but clearly hamper the capability of finding a correct regulation category. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge graph

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data Pre-processing and Visualization for machine learning Models and its applications in Education 8

Data Pre-processing and Visualization for Machine Learning M...

引用

8th international conference on Digital Technologies in Education, science and Industry, DTESI 2023

作者： Borodkin, Konstantin Nurtas, Marat Altaibek, Aizhan Daineko, Yevgeniya Otepov, Temirlan International Information Technology University Manas St. 34/1 Almaty050040 Kazakhstan

this research study explores the role and degree of influence of data pre-processing techniques in the development and application of machine learning models for solving prediction tasks within the domain of education. Effective data visualization techniques are essential for understanding trends, patterns, and relationships within the data, aiding in feature selection, model evaluation, and interpretation. the research study deals with various techniques for improving data quality, such as data cleaning, working with missing values, and data selection. We assume that data quality and the use of different preprocessing techniques can have a significant impact on some machine learning models performance and quality. © 2023 Copyright for this paper by its authors.

关键词： machine learning

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Deep learning Based Pepper Maturity Detection 24

Deep Learning Based Pepper Maturity Detection

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Ma, Yiqun Peng, Ziyang Ruan, Yujie College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

China is one of the world’s important pepper production area, the existing picking method still rely on manual picking method for picking, and pepper is a kind of batch maturity of the herb, manual picking is not only time-consuming but also very laborious, and the pepper target detection technology is the key to realize the automatic picking of pepper, the existing research for pepper target detection is relatively small, this paper intends to choose the image processing and machine learning methods for pepper to carry out target detection. First of all, the processed pepper image data are passed into Yolov5s, Yolov5m, Faster R-CNN, SSD to compare the four common deep learning methods, the comprehensive comparison in the pepper recognition stage Yolov5s is the most appropriate deep learning algorithm in the deep learning algorithms mentioned above, and in the second stage, using the binary classification method, the pepper data are divided into immature pepper and mature pepper, and directly sent to Yolov5s. the second stage uses a binary classification method to classify pepper data into immature and mature pepper, which is directly fed into the Yolov5s model with an accuracy of 97.4% for pepper maturity discrimination, providing diversified detection and corresponding technical support for the intelligent pepper picking summary. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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PAR-DeepONet: A Novel Physics Informed Operator learning Method Based On Physical Adaptive Refinement 24

PAR-DeepONet: A Novel Physics Informed Operator Learning Met...

引用

8th international conference on Computer science and Artificial Intelligence, CSAI 2024

作者： Zhou, Yu Chen, Xinhai Liu, Jie National University of Defense Technology ChangSha ChangSha China

ISBN: (纸本)9798400718182

Operator learning methods have been extensively employed for solving partial differential equations in various scientific and engineering problems. Existing operator learning methods primarily rely on data-driven approaches for model training. However, these methods suffer from inadequate adherence to physical laws and limited utilization of training data, leading to reduced accuracy, especially for solutions involving data with low magnitudes, often referred to as small-value data. To address these issues, we propose PAR-DeepONet, a novel physics-informed operator learning method based on physical adaptive refinement. To ensure the predictive solutions of the model adhere to physical laws, PAR-DeepONet trains the model by incorporating initial conditions, boundary conditions, and equation constraints. this approach enables the model to assimilate physical information during training. Furthermore, we integrated a physical adaptive refinement mechanism to improve the utilization of small-value data in the training process. We conduct experiments on four equations to evaluate the effectiveness of our method, and the results demonstrate that our method outperforms existing operator learning methods, achieving maximum an impressive 71.3% improvement in accuracy. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Preprocessy: A Customisable data Preprocessing Framework with High-Level APIs 7

Preprocessy: A Customisable Data Preprocessing Framework wit...

引用

7th international conference on data science and machine learning applications (cdma)

作者： Kazi, Saif Vakharia, Priyesh Shah, Parth Gupta, Riya Tailor, Yash Mantry, Palak Rathod, Jash Veermata Jijabai Technol Inst Dept Comp Engn & Informat Technol Mumbai Maharashtra India

ISBN: (纸本)9781665410144

data preprocessing is an important prerequisite for data mining and machine learning. In this paper, we introduce Preprocessy, a Python framework that provides customisable data preprocessing pipelines for processing structured data. Preprocessy pipelines come with sane defaults and the framework also provides low-level functions to build custom pipelines. the paper gives a brief overview of the features and the high-level APIs of Preprocessy along with a performance comparison against Scikit-learn and Pandas on two datasets. Preprocessy provides functions for handling missing data and outliers, data normalisation, feature selection and data sampling. the goal of Preprocessy is to be easy to use, flexible and performant. Preprocessy helps beginners and experts alike by making data preprocessing an easier and faster task.

关键词： python data preprocessing pipelines machine learning data mining data science

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Optimizing Feature Selection in Spam Email Detection Using Co-Evolutionary Algorithms 8

Optimizing Feature Selection in Spam Email Detection Using C...

引用

8th SLAAI - international conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Wanigasingha, Nethni Hirusha Fernando, T.G.I. University of Sri Jayewardenepura Department of Computer Science Nugegoda Sri Lanka

ISBN: (纸本)9798331509170

the Co-Evolutionary Algorithms for Feature Selections delve into feature selection in the context of data-rich environments. the study aims to identify and implement a suitable co-evolutionary feature selection method for the classification of spam emails. By exploring co-evolutionary algorithms and their application in solving feature selection challenges, the research investigates the transformation of feature selection issues into optimization problems. through the utilization of co-evolutionary algorithms, the study analyzes and compares the outcomes with other established feature selection methods in machine learning. the findings highlight the effectiveness of co-evolutionary algorithms in feature selection, particularly in the classification of spam emails, showcasing their potential for broader applications in text classification tasks. the paper contributes to the understanding of feature selection methodologies and their practical implications in enhancing classification accuracy and efficiency in data processing tasks. © 2024 IEEE.

关键词： Adversarial machine learning

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